Waleed Alnumay’s Post

Python Coding Tip Why You Should Use isinstance() for Safer Type Checking One of the safest ways for validating types in Python is with the use of isinstance (). Python is dynamically typed. That flexibility is powerful, but it also means your variables can hold unexpected values especially when working with user input, API responses, JSON data, AI agent states, or external libraries. This is where isinstance() becomes essential. isinstance() allows you to safely check whether an object belongs to a specific type and more importantly, it respects inheritance. Unlike type(), isinstance() correctly recognizes subclasses, making your code more robust and future-proof. Why isinstance() matters in real-world applications: • Prevents runtime errors before operations are performed • Supports inheritance-aware type checking • Enables validation of multiple types at once • Encourages defensive programming • Makes systems more stable and production-ready In dynamic systems such as AI agents, state-driven workflows, backend services, or data pipelines, assumptions about data types can easily break your application. A simple type check using isinstance() can prevent unexpected crashes and improve reliability. Clean Python code is not just about writing logic that works. It’s about writing code that anticipates failure and handles it gracefully. If you’re serious about writing professional, production-level Python Code, isinstance() should be part of your toolkit. Subscribe to my YouTube Channel where I teach AI Engineering and Coding in general. https://lnkd.in/ePQf7EPh

We asked 278 Python developers about how they use AI for coding. The pattern was consistent: AI helps with small, isolated tasks, but breaks down on real projects. Context gets lost. Code gets pasted back and forth. Files the AI doesn't know about break when you apply its suggestions. 65% said they're stuck at this point. The problem isn't the AI. It's the workflow. Agentic coding tools like Claude Code work differently. Instead of chatting in a browser, the AI runs in your terminal. It reads your files, edits them directly, runs your tests, sees the errors, and fixes them. It works across your whole codebase, not one snippet at a time. We're running a 2-day live course (March 21-22) where you'll build a complete Python CLI application from scratch using this workflow. Not toy examples, but a real project with Click, Textual, uv, git, and tests. You'll leave with a working project and a portable set of skills you can apply to your own code. Details and enrollment: https://lnkd.in/gvS-KzVn

SpeechRecognition library is the simplest way to add voice to any Python app. Works with most mics, no extra config needed. https://lnkd.in/gha3U45x

Are you seeking a more powerful, secure, and efficient way to manage dynamic expressions and policies within your Python projects? We are thrilled to announce the open-source release of CEL-expr-python, a native Python implementation of the Common Expression Language (CEL). CEL is renowned for its simplicity, speed, safety, and portability, making it an ideal solution for applications requiring decision-making, data validation, or rule enforcement. This new implementation, maintained by the CEL team, provides a robust Python API, built upon the stable C++ core, ensuring seamless integration and immediate access to the latest features and optimizations. CEL-expr-python is designed to empower Python developers working with dynamic expressions, policy enforcement, and data validation. If your work involves evaluating expressions loaded from external sources, enforcing clear and secure policies, or validating data against predefined rules, then this tool is precisely what you need. By leveraging CEL-expr-python, you can harness the proven benefits of CEL, including guaranteed side-effect-free and terminating expressions for enhanced safety, efficient evaluation speeds, and language-agnostic portability. This allows you to seamlessly integrate this potent technology into your existing Python stack. We invite you to explore the capabilities of CEL-expr-python and contribute to its growing ecosystem. Discover how it can streamline your development process and enhance the robustness of your applications. We are eager to hear about your experiences and feedback, so please share your thoughts via the GitHub issue queue. Explore the repository and delve into the accompanying codelab for a comprehensive understanding of how to get started and unlock the full potential of CEL within your Python projects. 🚀💡🔗

Are you seeking a more powerful, secure, and efficient way to manage dynamic expressions and policies within your Python projects? We are thrilled to announce the open-source release of CEL-expr-python, a native Python implementation of the Common Expression Language (CEL). CEL is renowned for its simplicity, speed, safety, and portability, making it an ideal solution for applications requiring decision-making, data validation, or rule enforcement. This new implementation, maintained by the CEL team, provides a robust Python API, built upon the stable C++ core, ensuring seamless integration and immediate access to the latest features and optimizations. CEL-expr-python is designed to empower Python developers working with dynamic expressions, policy enforcement, and data validation. If your work involves evaluating expressions loaded from external sources, enforcing clear and secure policies, or validating data against predefined rules, then this tool is precisely what you need. By leveraging CEL-expr-python, you can harness the proven benefits of CEL, including guaranteed side-effect-free and terminating expressions for enhanced safety, efficient evaluation speeds, and language-agnostic portability. This allows you to seamlessly integrate this potent technology into your existing Python stack. We invite you to explore the capabilities of CEL-expr-python and contribute to its growing ecosystem. Discover how it can streamline your development process and enhance the robustness of your applications. We are eager to hear about your experiences and feedback, so please share your thoughts via the GitHub issue queue. Explore the repository and delve into the accompanying codelab for a comprehensive understanding of how to get started and unlock the full potential of CEL within your Python projects. 🚀💡🔗

Are you seeking a more powerful, secure, and efficient way to manage dynamic expressions and policies within your Python projects? We are thrilled to announce the open-source release of CEL-expr-python, a native Python implementation of the Common Expression Language (CEL). CEL is renowned for its simplicity, speed, safety, and portability, making it an ideal solution for applications requiring decision-making, data validation, or rule enforcement. This new implementation, maintained by the CEL team, provides a robust Python API, built upon the stable C++ core, ensuring seamless integration and immediate access to the latest features and optimizations. CEL-expr-python is designed to empower Python developers working with dynamic expressions, policy enforcement, and data validation. If your work involves evaluating expressions loaded from external sources, enforcing clear and secure policies, or validating data against predefined rules, then this tool is precisely what you need. By leveraging CEL-expr-python, you can harness the proven benefits of CEL, including guaranteed side-effect-free and terminating expressions for enhanced safety, efficient evaluation speeds, and language-agnostic portability. This allows you to seamlessly integrate this potent technology into your existing Python stack. We invite you to explore the capabilities of CEL-expr-python and contribute to its growing ecosystem. Discover how it can streamline your development process and enhance the robustness of your applications. We are eager to hear about your experiences and feedback, so please share your thoughts via the GitHub issue queue. Explore the repository and delve into the accompanying codelab for a comprehensive understanding of how to get started and unlock the full potential of CEL within your Python projects. 🚀💡🔗

Are you seeking a more powerful, secure, and efficient way to manage dynamic expressions and policies within your Python projects? We are thrilled to announce the open-source release of CEL-expr-python, a native Python implementation of the Common Expression Language (CEL). CEL is renowned for its simplicity, speed, safety, and portability, making it an ideal solution for applications requiring decision-making, data validation, or rule enforcement. This new implementation, maintained by the CEL team, provides a robust Python API, built upon the stable C++ core, ensuring seamless integration and immediate access to the latest features and optimizations. CEL-expr-python is designed to empower Python developers working with dynamic expressions, policy enforcement, and data validation. If your work involves evaluating expressions loaded from external sources, enforcing clear and secure policies, or validating data against predefined rules, then this tool is precisely what you need. By leveraging CEL-expr-python, you can harness the proven benefits of CEL, including guaranteed side-effect-free and terminating expressions for enhanced safety, efficient evaluation speeds, and language-agnostic portability. This allows you to seamlessly integrate this potent technology into your existing Python stack. We invite you to explore the capabilities of CEL-expr-python and contribute to its growing ecosystem. Discover how it can streamline your development process and enhance the robustness of your applications. We are eager to hear about your experiences and feedback, so please share your thoughts via the GitHub issue queue. Explore the repository and delve into the accompanying codelab for a comprehensive understanding of how to get started and unlock the full potential of CEL within your Python projects. 🚀💡🔗

Are you seeking a more powerful, secure, and efficient way to manage dynamic expressions and policies within your Python projects? We are thrilled to announce the open-source release of CEL-expr-python, a native Python implementation of the Common Expression Language (CEL). CEL is renowned for its simplicity, speed, safety, and portability, making it an ideal solution for applications requiring decision-making, data validation, or rule enforcement. This new implementation, maintained by the CEL team, provides a robust Python API, built upon the stable C++ core, ensuring seamless integration and immediate access to the latest features and optimizations. CEL-expr-python is designed to empower Python developers working with dynamic expressions, policy enforcement, and data validation. If your work involves evaluating expressions loaded from external sources, enforcing clear and secure policies, or validating data against predefined rules, then this tool is precisely what you need. By leveraging CEL-expr-python, you can harness the proven benefits of CEL, including guaranteed side-effect-free and terminating expressions for enhanced safety, efficient evaluation speeds, and language-agnostic portability. This allows you to seamlessly integrate this potent technology into your existing Python stack. We invite you to explore the capabilities of CEL-expr-python and contribute to its growing ecosystem. Discover how it can streamline your development process and enhance the robustness of your applications. We are eager to hear about your experiences and feedback, so please share your thoughts via the GitHub issue queue. Explore the repository and delve into the accompanying codelab for a comprehensive understanding of how to get started and unlock the full potential of CEL within your Python projects. 🚀💡🔗

Most Python developers stay stuck at average level… Not because they don’t work hard… But because they don’t know these small but powerful tricks. Today I’m sharing a FREE PDF that contains 👉 100 Python Tips & Tricks (Basic → Intermediate) This is the kind of stuff that: • Makes your code cleaner • Saves hours of time • Makes you stand out from 90% developers And the best part? These are practical shortcuts, not theory. 📌 Example things you’ll learn: Flatten nested lists in one line Merge dictionaries like a pro Use Python to automate real tasks Write cleaner & optimized code (Exactly the kind of knowledge most tutorials skip…) 💡 But here’s the truth: Knowing tricks ≠ Building real AI systems If you really want to move from Python → AI Engineer, you need to understand: 👉 RAG (Retrieval Augmented Generation) 👉 LangChain & LangGraph 👉 Real-world AI applications 🎯 That’s exactly why I created this: 🔥 LangGraph Mastery Course (Project-Based) 👉 Learn how to build real AI systems step-by-step 🔗 https://lnkd.in/dTz9H-8E ⚡ My suggestion: Go through this PDF Apply 5–10 tricks today Then move to building real-world AI projects If you found this helpful, comment “PYTHON” I’ll share more such resources 🚀 Pdf credit goes to respective owner. Follow Pratham Uday Chandratre for more!

🚀 Understanding Polymorphism in Python – OOPS Practice As part of strengthening my Python Object-Oriented Programming (OOPS) concepts, I practiced Polymorphism and its different implementations in Python. Polymorphism means “one interface, multiple behaviors.” It allows the same function, method, or operator to behave differently depending on the object. For example: 5 + 3 = 8 "Hello " + "World" = "Hello World" The same + operator works for numbers and strings but produces different results. This is a simple example of polymorphism. To understand this concept better, I implemented several examples using operator overloading and comparisons in Python. 📚 Concepts Practiced ✔ Operator Overloading (__add__, __sub__) ✔ Comparison Operators (__eq__, __lt__, __gt__) ✔ Object-to-object operations ✔ Implementing custom behavior for operators ✔ Writing clean class-based logic 🧑🎓 Student Class Examples • Adding marks of multiple students using + operator • Comparing student marks using ==, <, and > 🛒 Cart / Product Examples • Subtracting product prices using - operator • Comparing item prices inside a cart system 📖 Polymorphism Types Covered 🔹 Method Overloading Using the same method name with different arguments. 🔹 Method Overriding Child class redefining a method already defined in the parent class. 🔹 Constructor Overloading Initializing objects in multiple ways using default parameters. 🔹 Constructor Overriding Child class redefining the parent constructor. 🔹 Operator Overloading Allowing operators like +, -, ==, < to work with objects. 💡 Why Polymorphism is Important ✔ Improves code readability ✔ Encourages reusable code ✔ Makes applications flexible and scalable ✔ Reduces complexity in large systems Strong understanding of OOPS concepts is essential for: Backend Development System Design Writing scalable applications Learning step by step. Improving every day 💪 📄 Code and explanation attached in the PDF. 🙏 Thanks to 10000 Coders venubabu vajja Kotesh bommu and Akshaya Koyedi for continuously motivating me to learn and grow. #Python #OOPS #Polymorphism #OperatorOverloading #BackendDeveloper #CodingJourney #Learning #SoftwareDevelopment